US2025087733A1PendingUtilityA1
METHOD AND SYSTEM FOR ACCELERATED LIFE TESTING (ALT) OF PROTON EXCHANGE MEMBRANE FUEL CELLS (PEMFCs)
Est. expirySep 7, 2043(~17.1 yrs left)· nominal 20-yr term from priority
H01M 8/04552H01M 8/04992H01M 8/04671H01M 8/04305Y02E60/50G01R 31/385G01R 31/378G01R 31/367G01R 31/392G06F 17/16G06N 3/006G06N 3/084G06N 3/0442G06N 3/045G06F 18/217G06F 18/214G06F 18/15
70
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Claims
Abstract
Provided is a method and a system for accelerated life testing (ALT) of a proton exchange membrane fuel cell (PEMFC). In the present disclosure, a collected voltage-time sequence data of the PEMFC is filtered and subjected to empirical mode decomposition (EMD), such that a voltage data is decomposed to obtain K intrinsic mode functions. A constructed bidirectional long short-term memory-based artificial neural network (BiLSTM) shows majority input characteristics and can model each of the intrinsic mode functions independently, thereby reducing a difficulty of long-cycle life prediction in limited training data scenarios. In addition, optimal parameters of the BiLSTM are optimized through a sparrow search algorithm, which greatly improves a prediction accuracy of a remaining useful life for the PEMFC. The method and the system of the present disclosure exhibit low computing cost, simple parameter setting, and high prediction accuracy, and are extremely suitable for operation and maintenance of the PEMFC.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method for accelerated life testing (ALT) of a proton exchange membrane fuel cell (PEMFC), comprising the following steps:
step 1, conducting data collection and processing: collecting a voltage-time sequence data of the PEMFC through a sensor to allow Gaussian filtering to filter out noise and abnormal peaks to obtain a processed voltage-time sequence data; subjecting the processed voltage-time sequence data to empirical mode decomposition (EMD), such that a voltage data is decomposed to obtain K intrinsic mode functions, wherein K is an integer of greater than or equal to 1; and dividing the K intrinsic mode functions into a training data set and a test data set according to a ratio of user demand, and normalizing the training data set and normalizing the test data set based on a normalization standard of the training data set to smoothly map into [0,1]; step 2, constructing a bidirectional long short-term memory-based artificial neural network (BiLSTM), wherein the BiLSTM comprises an input layer, a hidden layer, and an output layer, a number of input eigenvalues of the BiLSTM is determined according to a number of the intrinsic mode functions, and a matrix and a vector of the BILSTM are initialized to 0; step 3, training the BiLSTM: subjecting the BiLSTM to network training based on an input data, selecting t time steps as a prediction interval, and using a data before each of the t time steps as an input training data at a current moment; selecting a root mean square error as an error function, calculating a gradient of each weight according to a corresponding error term using an adaptive matrix estimation algorithm as an optimizer when an error is greater than a default threshold, wherein the error term is propagated in a reverse direction along time and the weight is updated through stochastic gradient descent; conducting gradient evaluation, wherein if a gradient accuracy meets a stopping criterion, a corresponding value of the gradient accuracy is output as a prediction result; if the gradient accuracy does not meet the stopping criterion, the gradient is re-updated; and generating an initial sample point X i with an initial learning rate, a number of iterations, and a number of neurons in the hidden layer according to a range of model parameters, inputting the initial sample point X i into a sparrow search algorithm to allow automatic optimization on network parameters of the BiLSTM comprising the initial learning rate, the number of iterations, and the number of the neurons in the hidden layer, and then outputting optimal network parameters to obtain a trained and optimized BiLSTM; step 4, testing the BiLSTM: inputting the test data set into the trained and optimized BiLSTM to allow testing to determine whether a newly selected sample point meets a model accuracy requirement; wherein if the newly selected sample point meets the model accuracy requirement, the testing is terminated through a termination algorithm to output an optimal BILSTM; if the newly selected sample point does not meet the model accuracy requirement, whether the automatic optimization reaches a maximum number of iterations is determined; if the automatic optimization reaches the maximum number of iterations, the optimal BILSTM is output, otherwise the sparrow search algorithm is iterated in a loop until the newly selected sample point meets the model accuracy requirement; and step 5, applying the optimal BILSTM to the ALT of the PEMFC, denormalizing an obtained prediction result, and converting a resulting predicted remaining useful life data into a remaining useful life-time sequence data through the output layer.
2 . The method for ALT of a PEMFC according to claim 1 , wherein the Gaussian filtering in step 1 has a formula as follows:
K
(
t
)
=
exp
(
-
t
2
/
2
)
2
π
,
f
(
t
j
)
=
∑
i
=
1
n
s
i
·
u
(
t
j
)
/
∑
i
=
1
n
s
i
,
s
i
=
K
[
(
t
j
-
t
i
)
]
/
H
,
in the formula, K(t) represents standard normal distribution of a parameter data at time t, f(t j ) represents a filtered data, u(t j ) represents the parameter data, n represents a number of the parameter data, and H represents a bandwidth.
3 . The method for ALT of a PEMFC according to claim 2 , wherein a process of decomposing the voltage data in step 1 specifically comprises: smoothing the voltage data through the EMD, wherein the K intrinsic mode functions comprise local characteristic signals at different time scales of an original signal, respectively; and the EMD is suitable for PEMFC application scenarios with less monitoring data by decomposing the voltage-time sequence data and adding the training data of the BiLSTM to predict the ALT of a long-period PEMFC through a low-proportion training data.
4 . The method for ALT of a PEMFC according to claim 3 , wherein the input layer in step 2 has a calculation formula as follows:
i
t
=
σ
(
W
i
·
[
h
t
-
1
,
x
t
]
+
b
i
)
,
o
t
=
σ
(
W
o
·
[
h
t
-
1
,
x
t
]
+
b
o
)
,
f
t
=
σ
(
W
f
·
[
h
t
-
1
,
x
t
]
+
b
f
)
,
in the formula: i t , W t , and b t represent a calculation result, a weight matrix, and a bias term of the input layer, respectively; O t , W o , and b o represent a calculation result, a weight matrix, and a bias term of the output layer, respectively; f t , W f , and b f represent a calculation result, a weight matrix, and a bias term of a forget gate, respectively; h t−1 represents a next value of the hidden layer at time t−1, x t represents input information, and σ represents a sigmoid activation function; and
a value of memory information output by the output layer at time t is c t , a next value of memory information output by the output layer at time t−1 is c t−1 , a value of the hidden layer at time t is h t , and the next value of the hidden layer at time t−1 is h t−1 , wherein formulas are as follows:
c
t
~
=
tanh
(
W
c
·
[
h
t
-
1
,
x
t
]
+
b
c
)
c
t
=
f
t
·
c
t
-
1
+
i
t
·
c
t
~
h
t
=
o
t
·
tanh
(
c
t
)
,
in the formula: {tilde over (c)} t represents a candidate state of a memory unit at time t; tanh represents a hyperbolic tangent activation function; W c represents a weight matrix of an input unit; x t represents the input information; b c represents a bias term of an input unit state; and · represents element-wise multiplication.
5 . The method for ALT of a PEMFC according to claim 1 , wherein the adaptive matrix estimation algorithm in step 3 specifically comprises:
(1) randomly initializing all weights in the BiLSTM; (2) setting initial parameters of a first-order moment, a second-order moment, a global learning rate, and an attenuation coefficient; (3) calculating a current gradient through a loss function; (4) calculating the time steps; (5) updating an accumulated gradient with the current gradient to allow first-order moment estimation; (6) updating a square of the accumulated gradient with the current gradient to allow second-order moment estimation; (7) subjecting the first-order moment and the second-order moment to deviation correction; (8) calculating an update amount of the all weights in the BiLSTM through corrected first-order moment and second-order moment; (9) updating the all weights of parameters in the BILSTM; and (10) repeating steps (3) to (9) to allow iteration, terminating the iteration when a maximum number of iterations for termination is achieved, and outputting current parameters in the BiLSTM.
6 . The method for ALT of a PEMFC according to claim 5 , wherein the automatic optimization on the network parameters in step 3 specifically comprises:
randomly initializing a position of a sparrow population, setting a producer ratio and an optimization dimension, setting a position update mode of a discoverer at different warning values and calculating a fitness value, setting a position update mode of a follower with different fitness values, obtaining a final position as an optimal solution, and outputting the optimal solution to obtain the optimal network parameters to obtain the trained and optimized BiLSTM.
7 . A system for ALT of a PEMFC, comprising a data acquisition and processing module, a neural network (NN) construction module, an NN training module, an NN optimization module, and an NN application module; wherein
the data acquisition and processing module is configured to conduct: collecting a voltage-time sequence data of the PEMFC through a sensor to allow Gaussian filtering to filter out noise and abnormal peaks to obtain a processed voltage-time sequence data; subjecting the processed voltage-time sequence data to EMD, such that a voltage data is decomposed to obtain K intrinsic mode functions, wherein K is an integer of greater than or equal to 1; and dividing the K intrinsic mode functions into a training data set and a test data set according to a ratio, and normalizing the training data set and normalizing the test data set based on a normalization standard of the training data set to smoothly map into [0,1]; the NN construction module is configured to conduct: constructing a BiLSTM, wherein the BiLSTM comprises an input layer, a hidden layer, and an output layer, a number of input eigenvalues of the BiLSTM is determined according to a number of the intrinsic mode functions, and a matrix and a vector of the BiLSTM are initialized to 0; the NN training module is configured to conduct: subjecting the BILSTM to network training based on an input data, selecting t time steps as a prediction interval, and using a data before each of the t time steps as an input training data at a current moment; selecting a root mean square error as an error function, calculating a gradient of each weight according to a corresponding error term using an adaptive matrix estimation algorithm as an optimizer when an error is greater than a default threshold, wherein the error term is propagated in a reverse direction along time and the weight is updated through stochastic gradient descent; conducting gradient evaluation, wherein if a gradient accuracy meets a stopping criterion, a corresponding value of the gradient accuracy is output as a prediction result; if the gradient accuracy does not meet the stopping criterion, the gradient is re-updated; the NN optimization module is configured to conduct: generating an initial sample point X i with an initial learning rate, a number of iterations, and a number of neurons in the hidden layer according to a range of model parameters, inputting the initial sample point X i into a sparrow search algorithm to allow automatic optimization on network parameters of the BiLSTM comprising the initial learning rate, the number of iterations, and the number of the neurons in the hidden layer, and then outputting optimal network parameters to obtain a trained and optimized BiLSTM; inputting the test data set into the trained and optimized BILSTM to allow testing to determine whether a newly selected sample point meets a model accuracy requirement; wherein if the newly selected sample point meets the model accuracy requirement, the testing is terminated through a termination algorithm to output an optimal BiLSTM; if the newly selected sample point does not meet the model accuracy requirement, whether the automatic optimization reaches a maximum number of iterations is determined; if the automatic optimization reaches the maximum number of iterations, the optimal BILSTM is output, otherwise the sparrow search algorithm is iterated in a loop until the newly selected sample point meets the model accuracy requirement; and the NN application module is configured to conduct: applying the optimal BILSTM to the ALT of the PEMFC, denormalizing an obtained prediction result, and converting a resulting predicted remaining useful life data into a remaining useful life-time sequence data through the output layer.
8 . The system for ALT of a PEMFC according to claim 7 , wherein the input layer in the BiLSTM has a calculation formula as follows:
i
t
=
σ
(
W
i
·
[
h
t
-
1
,
x
t
]
+
b
i
)
,
o
t
=
σ
(
W
o
·
[
h
t
-
1
,
x
t
]
+
b
o
)
,
f
t
=
σ
(
W
f
·
[
h
t
-
1
,
x
t
]
+
b
f
)
,
in the formula: i t , W t , and b t represent a calculation result, a weight matrix, and a bias term of the input layer, respectively; O t , W o , and b o represent a calculation result, a weight matrix, and a bias term of the output layer, respectively; f t , W f , and b f represent a calculation result, a weight matrix, and a bias term of a forget gate, respectively; h t−1 represents a next value of the hidden layer at time t−1, x t represents input information, and σ represents a sigmoid activation function; and
a value of memory information output by the output layer at time t is c t , a next value of memory information output by the output layer at time t−1 is c t−1 , a value of the hidden layer at time t is h t , and the next value of the hidden layer at time t−1 is h t−1 , wherein formulas are as follows:
c
t
~
=
tanh
(
W
c
·
[
h
t
-
1
,
x
t
]
+
b
c
)
c
t
=
f
t
·
c
t
-
1
+
i
t
·
c
t
~
h
t
=
o
t
·
tanh
(
c
t
)
,
in the formula: {tilde over (c)} t represents a candidate state of a memory unit at time t; tanh represents a hyperbolic tangent activation function; W c represents a weight matrix of an input unit; x t represents the input information; b c represents a bias term of an input unit state; and · represents element-wise multiplication.
9 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 1 .
10 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 2 .
11 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 3 .
12 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 4 .
13 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 5 .
14 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to implement the steps of the method according to claim 6 .
15 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to runs the system according to claim 7 .
16 . A computer device for ALT of an PEMFC, comprising a memory, a processor, and a program instruction stored in the memory to allow execution by the processor, wherein the processor executes the program instruction to runs the system according to claim 8 .
17 . A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps in the method according to claim 1 .
18 . A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps in the method according to claim 2 .
19 . A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps in the method according to claim 3 .
20 . A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps in the method according to claim 4 .Join the waitlist — get patent alerts
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